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A flexible parametric competing-risks model using a direct likelihood approach for the cause-specific cumulative incidence function

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  • Sarwar Islam Mozumder

    (University of Leicester)

  • Mark J. Rutherford

    (University of Leicester)

  • Paul C. Lambert

    (University of Leicester)

Abstract

In competing-risks analysis, the cause-specific cumulative incidence function (CIF) is usually obtained in a modeling framework by either 1) transform- ing on all cause-specific hazards or 2) transforming by using a direct relationship with the subdistribution hazard function. We expand on current competing-risks methodology from within the flexible parametric survival modeling framework and focus on the second approach. This approach models all cause-specific CIFs si- multaneously and is more useful for answering prognostic-related questions. We propose the direct flexible parametric survival modeling approach for the cause- specific CIF. This approach models the (log cumulative) baseline hazard without requiring numerical integration, which leads to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and incorporate alternative link functions, for example, logit links. To implement these methods, we introduce a new estimation command, stpm2cr, and demon- strate useful predictions from the model through an illustrative melanoma dataset.

Suggested Citation

  • Sarwar Islam Mozumder & Mark J. Rutherford & Paul C. Lambert, 2017. "A flexible parametric competing-risks model using a direct likelihood approach for the cause-specific cumulative incidence function," Stata Journal, StataCorp LP, vol. 17(2), pages 462-489, June.
  • Handle: RePEc:tsj:stataj:v:17:y:2017:i:2:p:462-489
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    Cited by:

    1. Likun Cao & Ziwen Chen & James Evans, 2024. "Modularity, Higher-Order Recombination, and New Venture Success," Papers 2405.15042, arXiv.org.

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